GroupBy.nth()

GroupBy.nth(n, dropna=None) [source]

Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints.

If dropna, will take the nth non-null row, dropna is either Truthy (if a Series) or ?all?, ?any? (if a DataFrame); this is equivalent to calling dropna(how=dropna) before the groupby.

Parameters:

Parameters:	n : int or list of ints a single nth value for the row or a list of nth values dropna : None or str, optional apply the specified dropna operation before counting which row is the nth row. Needs to be None, ?any? or ?all?

n : int or list of ints

a single nth value for the row or a list of nth values

dropna : None or str, optional

apply the specified dropna operation before counting which row is the nth row. Needs to be None, ?any? or ?all?

Examples

>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2],
...                    'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B'])
>>> g = df.groupby('A')
>>> g.nth(0)
     B
A
1  NaN
2  3.0
>>> g.nth(1)
     B
A
1  2.0
2  5.0
>>> g.nth(-1)
     B
A
1  4.0
2  5.0
>>> g.nth([0, 1])
     B
A
1  NaN
1  2.0
2  3.0
2  5.0

Specifying dropna allows count ignoring NaN

>>> g.nth(0, dropna='any')
     B
A
1  2.0
2  3.0

NaNs denote group exhausted when using dropna

>>> g.nth(3, dropna='any')
    B
A
1 NaN
2 NaN

Specifying as_index=False in groupby keeps the original index.

>>> df.groupby('A', as_index=False).nth(1)
   A    B
1  1  2.0
4  2  5.0

Links:

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.core.groupby.GroupBy.nth.html

doc_Pandas

2025-01-10 15:47:30

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